An explanatory policy analysis of legislative change permitting pharmacists in Alberta, Canada, to prescribe
Bibliographic record
Abstract
OBJECTIVES: This paper provides an explanatory policy analysis of the new legislation which permits pharmacist prescribing in Alberta, Canada: the Pharmacists Profession Regulations (2006) to the Health Professions Act (1999). Its purpose is to provide useful insights for pharmacy regulatory bodies in other jurisdictions internationally that are in a position to pursue similar opportunities. METHODS: A search for government and regulatory body documents related to Alberta healthcare system and pharmacist prescribing was performed. Correspondence was initiated with authors and regulators to clarify or obtain current data. KEY FINDINGS: Research to support policy change recommendations and communication among healthcare professionals, regulators and other stakeholders is essential for developing and implementing legislative change regarding health professionals' scopes of practice at a time when legislative change is possible. Stakeholder barriers to implementation need to be identified early to provide opportunity to address and resolve. CONCLUSIONS: Collaboration between healthcare professionals, regulators and other stakeholders is essential to developing a prescribing model that can be successfully implemented when there is the opportunity for legislative change related to health professionals' scope of practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".